The family planning quotient and reproductive life index (FPQ/RepLI) tool: a solution for family planning, reproductive life planning and contraception counseling.

OBJECTIVE: Access to comprehensive and culturally appropriate reproductive life planning is essential to women's health. Although many strategies and tools exist, few are designed for longitudinal use or provide visual aids. Our objective is to present the Family Planning Quotient (FPQ) and Reproductive Life Index (RepLI) (FPQ/RepLI) tool we created to facilitate the discussion of family planning and reproductive life goals between patients and providers and to provide a summary our evaluation of the tool. This tool was developed as a response to the Centers for Disease Control and Prevention's charge of developing a tool that could help facilitate reproductive life planning by giving the patient a better understanding of their reproductive goals and trajectory. STUDY DESIGN: This cross-sectional evaluation of our tool took place with patients and providers at an urban, public hospital in Chicago. Patients spoke with a health educator about their sexual, gynecological, and obstetric history to complete the FPQ/RepLI tool. Our primary objective was to measure the proportion of women who indicated the tool was helpful and that they would use it to track their reproductive goals. MAIN OUTCOME MEASURES: Patients and providers completed an evaluation survey rating their satisfaction with the tool. Survey responses were summarized using frequencies and percentages. RESULTS: During the study, 790 patients completed the evaluation.. Most patients (n = 725, 91.9%) agreed that the tool was helpful and that they would use it to track their reproductive goals. Fifty-five (83.5%) providers agreed that there is a need for reproductive health tools in clinical practice. CONCLUSIONS: Most agreed that the tool helped the patient communicate goals, aided in educating about contraception, and facilitated the discussion and decision-making process about available contraceptives. The tool gives patients a resource for family and reproductive goal planning. Broad dissemination amongst other medical specialties beyond obstetrics and gynecology may make reproductive life planning accessible to more women.

Machine learning ensemble models predict total charges and drivers of cost for transsphenoidal surgery for pituitary tumor.

OBJECTIVE: Efficient allocation of resources in the healthcare system enables providers to care for more and needier patients. Identifying drivers of total charges for transsphenoidal surgery (TSS) for pituitary tumors, which are poorly understood, represents an opportunity for neurosurgeons to reduce waste and provide higher-quality care for their patients. In this study the authors used a large, national database to build machine learning (ML) ensembles that directly predict total charges in this patient population. They then interrogated the ensembles to identify variables that predict high charges. METHODS: The authors created a training data set of 15,487 patients who underwent TSS between 2002 and 2011 and were registered in the National Inpatient Sample. Thirty-two ML algorithms were trained to predict total charges from 71 collected variables, and the most predictive algorithms combined to form an ensemble model. The model was internally and externally validated to demonstrate generalizability. Permutation importance and partial dependence analyses were performed to identify the strongest drivers of total charges. Given the overwhelming influence of length of stay (LOS), a second ensemble excluding LOS as a predictor was built to identify additional drivers of total charges. RESULTS: An ensemble model comprising 3 gradient boosted tree classifiers best predicted total charges (root mean square logarithmic error = 0.446; 95% CI 0.439-0.453; holdout = 0.455). LOS was by far the strongest predictor of total charges, increasing total predicted charges by approximately $5000 per day.In the absence of LOS, the strongest predictors of total charges were admission type, hospital region, race, any postoperative complication, and hospital ownership type. CONCLUSIONS: ML ensembles predict total charges for TSS with good fidelity. The authors identified extended LOS, nonelective admission type, non-Southern hospital region, minority race, postoperative complication, and private investor hospital ownership as drivers of total charges and potential targets for cost-lowering interventions.

EPHB4 is a therapeutic target in AML and promotes leukemia cell survival via AKT.

EPHB4, an ephrin type B receptor, is implicated in the growth of several epithelial tumors and is a promising target in cancer therapy; however, little is known about its role in hematologic malignancies. In this article, we show that EPHB4 is highly expressed in ∼30% of acute myeloid leukemia (AML) samples. In an unbiased RNA interference screen of primary leukemia samples, we found that EPHB4 drives survival in a subset of AML cases. Knockdown of EPHB4 inhibits phosphatidylinositol 3-kinase/AKT signaling, and this is accompanied by a reduction in cell viability, which can be rescued by a constitutively active form of AKT. Finally, targeting EPHB4 with a highly specific monoclonal antibody (MAb131) is effective against AML in vitro and in vivo. EPHB4 is therefore a potential target in AML with high EPHB4 expression.